Databases are designed and utilized to store and organize information in a manner that facilitates fast access to desired data. For example, a user may wish to identify and/or modify the contents of a particular column, row, or individual cell of a database table.
In theory, such database interactions may be straightforward to implement. In many specific settings, however, there are practical limits to an extent to which such database operations may be performed in a desired manner. For example, a rate at which a database is capable of identifying and accessing/modifying stored data may reach a threshold or maximum level, beyond which results are not returned within an acceptable timeframe.
It is possible, again from a theoretical perspective, to provide sufficient hardware resources (i.e., processing power and/or memory) to manage virtually any number or rate of database operations, while providing virtually any desired response time. From a practical perspective, however, it may be prohibitively expensive to purchase and maintain such levels of hardware resources. Moreover, in settings in which a number of requests for database operations varies widely over time, the quantity of hardware resources required to manage the highest volume (e.g., peak volume) of database operations may be significantly larger than the quantity of hardware resources required during normal or low volumes of database operations. Consequently, at any given time in such settings, it may occur that a significant percentage of the hardware resources are not being utilized, and are therefore used inefficiently.
Thus, many database providers are faced with a suboptimal choice between extensive/inefficient hardware resources on the one hand, or providing users of the databases with unacceptably low levels of performance on the other. Meanwhile, the users (e.g., customers) may correspondingly be faced with either increased cost of use of the databases, and/or with experiences of database interactions that are inconvenient or unacceptable.